0
Research Papers: Fuel Combustion

Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm

[+] Author and Article Information
P. Ilamathi

Department of Production Engineering,
Government College of Technology,
Coimbatore - 641 013, India
e-mail: ilamathibala@gmail.com

V. Selladurai

Principal
Coimbatore Institute of Technology,
Coimbatore - 641 014, India
e-mail: selladurai.v@gmail.com

K. Balamurugan

Department of Mechanical Engineering,
Institute of Road and Transport Technology,
Erode - 638 316, India
e-mail: drkbalamurugan@gmail.com

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received October 13, 2011; final manuscript received December 7, 2012; published online May 24, 2013. Assoc. Editor: Gregory Jackson.

J. Energy Resour. Technol 135(3), 032201 (May 24, 2013) (4 pages) Paper No: JERT-11-1122; doi: 10.1115/1.4023328 History: Received October 13, 2011; Revised December 07, 2012

An approach to model coal combustion process to predict and minimize unburned carbon in bottom ash of a large-capacity pulverized coal-fired boiler used in thermal power plant is proposed. The unburned carbon characteristic is investigated by parametric field experiments. The effects of excess air, coal properties, boiler load, air distribution scheme, and nozzle tilt are studied. An artificial neural network (ANN) is used to model the unburned carbon in bottom ash. A genetic algorithm (GA) is employed to perform a search to determine the optimum level process parameters in ANN model which decreases the unburned carbon in bottom ash.

FIGURES IN THIS ARTICLE
<>
Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

The dimension of furnace and the arrangement of the burners

Grahic Jump Location
Fig. 3

The unburned carbon concentrations calculated by GA under various generations

Grahic Jump Location
Fig. 2

The Schematic diagram of a feed-forward neural network

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In